Fig. 2

aTRaIT processes a binary matrix D that stores the presence or absence of a variable in a sample (e.g., a mutation, a CNA, or a persistent epigenetic states). b.TRaIT merges the events occurring in the same samples (x₁, x₂ and x₄, merged to A), as the statistical signal for their temporal ordering is undistinguishable. The final model include such aggregate events. c. We estimate via bootstrap the prima facie ordering relation that satisfies Suppes’ conditions (Eq. 1) for statistical association. This induces a graph G_PF over variables x_i, which is weighted by information-theoretic measures for variables’ association such as mutual information or pointwise mutual information. dTRaIT employs heuristic strategies to remove loops from G_PF and produce a new graph G_NL [14]. eEdmonds’s algorithm can be used to reconstruct the optimal minimum spanning tree G_MO that minimises the weights in G_NL; here we use point-wise mutual information (pmi). f.Chow-Liu is a Bayesian mode-selection strategy that computes an undirected tree as a model of a joint distribution on the annotated variable. Then, we provide edge direction (temporal priority), with Suppes’ condition (Eq. 1) on marginal probabilities. Therefore, confluences are possible in the output model G_MO in certain conditions